A method that includes using a point spread function to de-blur an original motion invariant image to create a modified motion invariant image; using an edge detector to find edges in the modified motion invariant image; determining the distances between the edges and corresponding artifacts in the modified motion invariant image; using the distances between the edges and the corresponding artifacts to estimate a velocity of an object in the modified motion invariant image; generating a corrected point spread function corresponding to the estimated velocity of the object; and using the corrected point spread function to de-blur the original motion invariant image and create a resulting image.
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1. An electronic system, comprising: a processor; and a memory device coupled to the processor and having a program stored thereon for execution by the processor to: use a point spread function to de-blur an original motion invariant image to create a modified motion invariant image; use an edge detector to find edges in the modified motion invariant image; determine the distances between the edges and corresponding artifacts in the modified motion invariant image; use the distances between the edges and the corresponding artifacts to estimate a velocity of an object in the modified motion invariant image; generate a corrected point spread function corresponding to the estimated velocity of the object; and use the corrected point spread function to de-blur the original motion invariant image and create a resulting image.
An electronic system de-blurs images affected by motion. The system uses a processor and memory. Software in the memory uses a point spread function (PSF) to initially de-blur a motion-blurred image, creating a modified image. An edge detector finds edges within this modified image. The system measures the distances between these edges and their corresponding blur artifacts in the modified image. These distances are used to estimate the velocity of the moving object. Based on the estimated velocity, a corrected PSF is generated. Finally, this corrected PSF is used to de-blur the original motion-blurred image, producing a clearer, resulting image.
2. The system of claim 1 , wherein the processor-readable medium includes instructions that, when performed by the processor, configure the system to use a point spread function to de-blur an original motion invariant image to create a modified motion invariant image by using a point spread function corresponding to a stationary object in the original motion invariant image.
The system de-blurs images as described above. The initial de-blurring step, where a point spread function is used on a motion-blurred image to create a modified image, specifically employs a point spread function that represents a stationary object. This assumes an initial state of no motion to simplify the initial de-blurring before velocity estimation.
3. The system of claim 1 , wherein the processor-readable medium includes instructions that, when performed by the processor, configure the system to use an edge detector to find edges in the modified motion invariant image by using an edge detector to find edges of at least one object in the modified motion invariant image.
The system de-blurs images as described above. The edge detection step, where an edge detector finds edges in the modified image, specifically identifies the edges of one or more objects present in the modified, partially de-blurred image. This allows the system to locate distinct features for artifact distance measurement and velocity estimation.
4. The system of claim 3 , wherein the processor-readable medium includes instructions that, when performed by the processor, configure the system to use an edge detector to find edges of at least one object in the modified motion invariant image by detecting edges in a specific region of the modified motion invariant image.
The system de-blurs images as described above. The edge detection of object edges within the modified image is further refined by focusing the edge detection process on a specific region of interest within the modified image. This allows for targeted analysis of areas most likely to contain relevant edges and artifacts, improving efficiency and accuracy of velocity estimation.
5. The system of claim 1 , wherein the processor-readable medium includes instructions that, when performed by the processor, configure the system to determine the distances between the edges and corresponding artifacts in the modified motion invariant image by using changes in pixel intensity over a range of positions in the modified motion invariant image.
The system de-blurs images as described above. Determining the distance between edges and blur artifacts in the modified image involves analyzing changes in pixel intensity across a range of positions in the modified image. This method uses the gradient of pixel brightness to pinpoint the location of edges and the extent of the blur, thus enabling accurate distance measurement.
6. The system of claim 1 , wherein the processor-readable medium includes instructions that, when performed by the processor, configure the system to use the distances between the edges and the corresponding artifacts to estimate the velocity of the object in the modified motion invariant image by using the data table to retrieve the estimated velocity based on the distance.
The system de-blurs images as described above. Estimating object velocity from the measured distances between edges and blur artifacts utilizes a data table. This table stores pre-calculated velocity values corresponding to various distance measurements. The system retrieves the estimated velocity directly from the table based on the measured distance, providing a fast and efficient velocity estimation.
7. A method, comprising: using a point spread function to de-blur an original motion invariant image to create a modified motion invariant image; using an edge detector to find edges in the modified motion invariant image; determining the distances between the edges and corresponding artifacts in the modified motion invariant image; using the distances between the edges and the corresponding artifacts to estimate a velocity of an object in the modified motion invariant image; generating a corrected point spread function corresponding to the estimated velocity of the object; and using the corrected point spread function to de-blur the original motion invariant image and create a resulting image.
A method de-blurs images affected by motion. The method uses a point spread function (PSF) to initially de-blur a motion-blurred image, creating a modified image. An edge detector finds edges within this modified image. The distances between these edges and their corresponding blur artifacts in the modified image are measured. These distances are used to estimate the velocity of the moving object. Based on the estimated velocity, a corrected PSF is generated. Finally, this corrected PSF is used to de-blur the original motion-blurred image, producing a clearer, resulting image.
8. The method of claim 7 wherein using a point spread function to de-blur an original motion invariant image to create a modified motion invariant image includes using a point spread function corresponding to a stationary object in the original motion invariant image.
The method de-blurs images as described above. The initial de-blurring step, where a point spread function is used on a motion-blurred image to create a modified image, specifically employs a point spread function that represents a stationary object. This assumes an initial state of no motion to simplify the initial de-blurring before velocity estimation.
9. The method of claim 7 wherein using an edge detector to find edges in the modified motion invariant image includes using an edge detector to find edges of at least one object in the modified motion invariant image.
The method de-blurs images as described above. The edge detection step, where an edge detector finds edges in the modified image, specifically identifies the edges of one or more objects present in the modified, partially de-blurred image. This allows the system to locate distinct features for artifact distance measurement and velocity estimation.
10. The method of claim 9 wherein using an edge detector to find edges of at least one object in the modified motion invariant image includes detecting edges in a specific region of the modified motion invariant image.
The method de-blurs images as described above. The edge detection of object edges within the modified image is further refined by focusing the edge detection process on a specific region of interest within the modified image. This allows for targeted analysis of areas most likely to contain relevant edges and artifacts, improving efficiency and accuracy of velocity estimation.
11. The method of claim 7 wherein determining the distances between the edges and corresponding artifacts in the modified motion invariant image includes using changes in pixel intensity over a range of positions in the modified motion invariant image.
The method de-blurs images as described above. Determining the distance between edges and blur artifacts in the modified image involves analyzing changes in pixel intensity across a range of positions in the modified image. This method uses the gradient of pixel brightness to pinpoint the location of edges and the extent of the blur, thus enabling accurate distance measurement.
12. The method of claim 7 , wherein generating the corrected point spread function corresponding to the estimated velocity of the object includes generating the corrected point spread function corresponding to multiple velocity estimates, wherein each of the velocity estimates has a corresponding probability.
The method de-blurs images as described above. When generating the corrected PSF based on estimated velocity, the method can generate multiple corrected PSFs. Each PSF corresponds to a different possible velocity estimate. Each of these velocity estimates is associated with a probability, indicating the likelihood of that velocity being the correct one. This allows for a more robust de-blurring process by considering multiple possible motion scenarios.
13. A computer-readable storage device having instructions for causing a computer to implement a method, the method comprising: using a point spread function to de-blur an original motion invariant image; using an edge detector to find edges in the modified motion invariant image; determining the distances between the edges and corresponding artifacts in the modified motion invariant image; using the distances between the edges and the corresponding artifacts to estimate a velocity of an object in the modified motion invariant image; generating a corrected point spread function corresponding to the estimated velocity of the object; and using the corrected point spread function to de-blur the original motion invariant image and create a resulting image.
A computer-readable storage device contains instructions that, when executed by a computer, perform the following steps to de-blur images affected by motion: using a point spread function (PSF) to initially de-blur a motion-blurred image, creating a modified image; using an edge detector to find edges within this modified image; measuring the distances between these edges and their corresponding blur artifacts in the modified image; using these distances to estimate the velocity of the moving object; generating a corrected PSF based on the estimated velocity; and using the corrected PSF to de-blur the original motion-blurred image, producing a clearer, resulting image.
14. The computer-readable storage device of claim 13 , the device having instructions for causing the computer to implement the method, wherein using a point spread function to de-blur an original motion invariant image to create a modified motion invariant image includes using a point spread function corresponding to a stationary object in the original motion invariant image.
The computer-readable storage device with de-blurring instructions as described above specifies that the initial de-blurring step, where a point spread function is used on a motion-blurred image to create a modified image, employs a point spread function that represents a stationary object. This assumes an initial state of no motion to simplify the initial de-blurring before velocity estimation.
15. The computer-readable storage device of claim 13 , the device having instructions for causing the computer to implement the method, wherein using an edge detector to find edges in the modified motion invariant image includes using an edge detector to find edges of at least one object in the modified motion invariant image.
The computer-readable storage device with de-blurring instructions as described above specifies that the edge detection step, where an edge detector finds edges in the modified image, identifies the edges of one or more objects present in the modified, partially de-blurred image. This allows the system to locate distinct features for artifact distance measurement and velocity estimation.
16. The computer-readable storage device of claim 15 , the device having instructions for causing the computer to implement the method, wherein using an edge detector to find edges of at least one object in the modified motion invariant image includes detecting edges in a specific region of the modified motion invariant image.
The computer-readable storage device with de-blurring instructions as described above further refines the edge detection of object edges within the modified image by focusing the edge detection process on a specific region of interest within the modified image. This allows for targeted analysis of areas most likely to contain relevant edges and artifacts, improving efficiency and accuracy of velocity estimation.
17. The computer-readable storage device of claim 13 , the device having instructions for causing the computer to implement the method, wherein determining the distances between the edges and corresponding artifacts in the modified motion invariant image includes using changes in pixel intensity over a range of positions in the modified motion invariant image.
The computer-readable storage device with de-blurring instructions as described above specifies that determining the distance between edges and blur artifacts in the modified image involves analyzing changes in pixel intensity across a range of positions in the modified image. This method uses the gradient of pixel brightness to pinpoint the location of edges and the extent of the blur, thus enabling accurate distance measurement.
18. The computer-readable storage device of claim 13 , the device having instructions for causing the computer to implement the method, wherein using the distances between the edges and the corresponding artifacts to estimate the velocity of the object in the modified motion invariant image includes using the data table to retrieve the estimated velocity based on the distance.
The computer-readable storage device with de-blurring instructions as described above specifies that estimating object velocity from the measured distances between edges and blur artifacts utilizes a data table. This table stores pre-calculated velocity values corresponding to various distance measurements. The system retrieves the estimated velocity directly from the table based on the measured distance, providing a fast and efficient velocity estimation.
19. The computer-readable storage device of claim 13 , the device having instructions for causing the computer to implement the method, wherein generating the corrected point spread function corresponding to the estimated velocity of the object includes generating the corrected point spread function corresponding to multiple velocity estimates, wherein each of the velocity estimates has a corresponding probability.
The computer-readable storage device with de-blurring instructions as described above specifies that when generating the corrected PSF based on estimated velocity, the process can generate multiple corrected PSFs. Each PSF corresponds to a different possible velocity estimate. Each of these velocity estimates is associated with a probability, indicating the likelihood of that velocity being the correct one. This allows for a more robust de-blurring process by considering multiple possible motion scenarios.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
March 18, 2014
May 16, 2017
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